Github user OopsOutOfMemory commented on a diff in the pull request:
https://github.com/apache/spark/pull/6551#discussion_r31886263
--- Diff: sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
---
@@ -39,6 +39,16 @@ class SQLQuerySuite extends QueryTest with
BeforeAndAfterAll with SQLTestUtils {
val sqlContext = TestSQLContext
import sqlContext.implicits._
+ test("SPARK-8010: promote numeric to string") {
+ val df = Seq((1, 1)).toDF("key", "value")
+ df.registerTempTable("src")
+ val queryCaseWhen = sql("select case when true then 1.0 else '1' end
from src ")
+ val queryCoalesce = sql("select coalesce(null, 1, '1') from src ")
--- End diff --
@yhuai, actually we often write querys by using udf in then value and else
value, like below:
`select case when boolean then split(city_code, ',')[0] else -99 end from
tablename `
Hive will implicit convert the case when expression value to a string type
since `split function` returns string type but `else value` is a integer.
Spark sql current will throw exceptions because the types of then value and
else value is not convertible.
Why we use `StringType` is because when do implicit conversion in
AtomicType, almost every type meets `StringType` will be converted to
`StringType` except `BinaryType` and `BooleanType`
You can refer the chart at the bottom of the page:
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Types
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